Method for gathering traffic analytics data about a communication network

ABSTRACT

There is provided a method for gathering traffic analytics data about a communication network that analyzes specific attributes of communications relationships between system assets of a network.

BACKGROUND OF THE INVENTION

US Published Patent Application No. 2011/0246376 to Devakondra et alpoints out that network data processing systems are used for a varietyof different purposes and come in a number of different forms. Severaltypes of network data processing systems are commonly used by companiesand other organizations and may include, for example, local areanetworks, wide area networks, virtual private networks, and othersuitable types of networks. In addition to such networks that may bemaintained by the network operator itself, cloud services are availableand the users of this type of network data processing systems neitherown nor manage the physical infrastructure.

According to US Published Patent Application Number 2011/0270968 toSalsburg at al, the term “cloud computing” generally refers to a modelthat makes computing resources available over a network as services.Computing services provided in a cloud computing environment can bebroadly divided into three categories: (a) Infrastructure-as-a-Service(“IaaS”) generally seen as comprising the delivery of computer hardware(e.g., servers, data storage systems, routers, etc.) as a service, (b)Platform-as-a-Service (“PaaS”) generally seen as comprising the deliveryof a computing platform or solution stack as a service, and (c)Software-as-a-Service (“SaaS”) generally seen as comprising hostingcomplete applications and delivering the applications as a service.

A “cloud” is a set of computing resources, such as computer hardware,data storage, networks, applications, services, and Interfaces, thatallow computing to be delivered as a service. A cloud can be a privatecloud, a public cloud, or a hybrid cloud that combines both public andprivate clouds. A private cloud typically includes a data center orproprietary network that provides computing services to a group ofpeople, an organization, a business, or another entity. A private cloudmay be located within an organization's private network or within aprivate space dedicated to an organization within a cloud vendor datacenter. A public cloud is a cloud in which computing services are madeavailable to the public, typically for a fee. For example, a cloudservice provider may make computing resources available to anorganization via the Internet. A public cloud may be configured as a webservice that allows users to manage computing resources hosted by thepublic cloud via a web interface.

In a public cloud environment, computing resources are provided to auser on demand and In various sizes and configurations. For example, auser may utilize a public cloud for storing a small amount of data orfor hosting processor intensive software applications. A user can alsorequest additional resources on demand and de-allocate resources whenthey are no longer required. This flexibility and elasticity has madecloud computing attractive to many businesses and IT professionals. Inaddition to this flexibility and elasticity, cloud computing can enablean organization to reduce capital expenses normally allocated to ITinfrastructure.

However, there are many factors to consider before an organization movesa computing workload to a public cloud. For example, according to USPublished Patent Application Number 2011/0270968 to Salsburg et al,there is a need to validate business applications (workloads) in termsof technical portability and business requirements/compliance so thatthe workloads can be deployed into a cloud without considerablecustomization. Conventionally, according to US Published PatentApplication Number 2011/0270968 to Salsburg et al, this validation isaccomplished using a manual, time consuming process for workloadidentification, workload classification, and cloud provider assessmentto find the ‘best-fit’ for business workload hosting.

Organizations that employ more traditional types of network dataprocessing systems may contemplate whether to change over from theirmore traditional network environment to a cloud network environment. Inview of the fact that the particular cloud services offered by eachcloud service provider or vendor will have different features, benefits,service operating requirements, and costs, it would be advantageous fora network operator to have access to tools that can help guide adecision to migrate computing tasks to a cloud. Moreover, it would beadvantageous if such tools for a guiding a network operator could equipthe network operator to have an accurate picture of the computingresources in its own network that will or can be replaced by the cloudcomputing resources. Furthermore, network operators can make betterinformed decisions about purchasing cloud services if they can getpricing information about potential cloud service providers and,especially, pricing information about the scope of computing resourcesthat tasks could be taken over in a cloud service arrangement.

SUMMARY OF THE INVENTION

One object of the present invention is to provide a method for gatheringtraffic analytics data about a communication network that includes thesteps of creating an inventory collection of system assets, ascribing,with respect to a reference set of communication signals, a touchstonetag to each communication signal, analyzing the communication trafficcomprised of touchstone-tagged communication signals having transitrelationships with certain network resources, and deriving trafficanalytics data from the analysis. In connection with further details ofthe step of creating an inventory collection of system assets, this stepincludes identifying system assets of the network, the system assetsincluding one or both (1) network resources that are operable todistribute communication signals that transit to and between thesenetwork resources with each network resource having a transitrelationship with a communication signal in that the network resourcemay originate a communication signal and/or receive a communicationsignal and (2) entity applications that direct distributions ofcommunication signals to and between several system assets. Also, thisstep of creating an inventory collection of system assets includes, withregard to selected ones of the system assets, assigning a locationassignment to the system asset that classifies the system asset ashaving a location associated with a respective one of the networkresources. In connection with further details of the step of ascribing atouchstone tag to each communication signal of a reference set ofcommunication signals, this step includes ascribing to eachcommunication signal a touchstone tag that indicates that the so-taggedcommunication signal has a transit relationship with a respective one ofthe network resources.

In connection with further details of the step of analyzing thecommunication traffic, this step involves, with respect to a firstresource location group which includes a first set of network resourceshaving a location attribute in common with one another and a secondresource location group having a location attribute in common with oneanother, the first set of network resources and the second set ofnetwork resources being different from one another, analyzing thecommunication traffic comprised of touchstone-tagged communicationsignals having transit relationships with the network resources of thefirst set of network resources and the second set of network resources.

According to optional features of the method of the present invention,the method may further include the step of assessing the trafficanalytics data relative to a selection of migration scenarios each ofwhich is a scenario wherein some or all of the functions performed bysome or all of the system assets would instead be performed externallyof the network. Furthermore, the method may optionally includegenerating a migration consideration set comprised of cost informationunits that each include cost information about one of the migrationscenarios.

According to one variation of the method of the present invention, themethod for gathering traffic analytics data about a communicationnetwork includes the following steps. The method is suitable foranalyzing traffic analytics of a communication network that includes aplurality of nodes and a plurality of connections between the nodes. Theone variation of the method includes performing a step of cataloging aninitial location, the step including: (a) identifying connectivityrelationships between the nodes of a first group of nodes of thenetwork, the identified connectivity relationships delimiting a firstconnectivity relationships set, (b) with respect to each identifiedconnectivity relationship of the first relationship set, determining ifthe respective identified connectivity relationship is in compliancewith a target array formation heuristic, whereupon each connectivityrelationship of the first connectivity relationship set is cataloguedinto a respective one of a target array comprised of those connectivityrelationships in compliance with the target array formation heuristicand a non-target array comprised of those connectivity relationships notIn compliance with the target array formation heuristic, and (c) withrespect to those nodes of the first group of nodes of the network whoseconnectivity relationships are In compliance with the target arrayformation heuristic, designating such nodes with a common locationdesignator that is unique from other location designators that have beenassociated, via a step of cataloging a location, with nodes of thenetwork.

The one variation of the method further includes performing a step ofcataloging a subsequent location, the step including: (a) with respectto a subsequent group of nodes of the network that is different fromother groups of nodes of the network that have been subjected to alocation cataloging step of the method, identifying connectivityrelationships between the nodes of the subsequent group of nodes, theidentified connectivity relationships delimiting a subsequentconnectivity relationship set, and the step of identifying connectivityrelationships between the nodes of the subsequent group of nodesincluding considering only those connectivity relationships that havenot previously been designated with a location designator during a stepof cataloging a location, (b) with respect to each identifiedconnectivity relationship of the subsequent connectivity relationshipset, determining if the respective identified connectivity relationshipis in compliance with the target array formation heuristic, whereuponeach connectivity relationship of the subsequent connectivityrelationship set is catalogued into a respective one of the target arraycomprised of those connectivity relationships in compliance with thetarget array formation heuristic and the non-target array comprised ofthose connectivity relationships not In compliance with the target arrayformation heuristic, and (c) with respect to those nodes of thesubsequent group of nodes of the network whose connectivityrelationships are in compliance with the target array formationheuristic, designating such nodes with with a common location designatorthat is unique from other location designators that have beenassociated, via a step of cataloging a location, with nodes of thenetwork. The one variation of the method additionally includes repeatingthe step of cataloging a subsequent location until at least two nodes ofthe network have been designated with a location designator; andanalyzing the communication traffic by referring to the locationdesignators and deriving traffic analytics data from the analysis.

A further object of the present invention is to provide a tangiblecomputer-readable medium for storing instructions for controlling acomputing device to generate an output, the instructions controlling thecomputing device to perform steps including the steps of creating aninventory collection of system assets, ascribing, with respect to areference set of communication signals, a touchstone tag to eachcommunication signal, analyzing the communication traffic comprised oftouchstone-tagged communication signals having transit relationshipswith certain network resources, and deriving traffic analytics data fromthe analysis.

Other aspects, embodiments and advantages of the present invention willbecome apparent from the following detailed description which, taken inconjunction with the accompanying drawings, illustrate the principles ofthe invention by way of example.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of various embodiments whenread together with the accompanying drawings, in which:

FIG. 1 is a schematic illustration of one possible sequence of stepsthat may be performed to implement the traffic analytics method of thepresent invention;

FIG. 2 is a schematic illustration of an exemplary network about whichthe method of the present invention can provide can provide rapidlyunderstandable and pertinent information for use by the network operatorin making decisions about migrating computing tasks to an alternativetask performer such as a private cloud computing resource or a publiccloud computing resource;

FIG. 3 is a schematic illustration of a plurality of geographic venueseach of which has a discrete collection of hardware assets variouslysituated at different geographic venues; and

FIG. 4 is a schematic illustration of a display that may be configuredon, and configured via, a dashboard platform for displaying informationabout the price qualified sets obtained via the method of the presentinvention;

FIG. 5 is a schematic plan view of a portion of the nodes of a network;

FIG. 6 is a schematic elevational view of a conceptualization of aninitial portion of a determining step of one variation of the method;

FIG. 7 is a schematic elevational view of a conceptualization of anotherportion of a determining step of one variation of the method; and

FIG. 8 is a schematic elevational view of a conceptualization of aportion of a determining step of one variation of the method.

DETAILED DESCRIPTION OF AN EMBODIMENT OF THE INVENTION

Reference is had to FIG. 1, which is a schematic illustration of onepossible sequence of steps that may be performed to implement thetraffic analytics method of the present invention, and the followingdescription of the traffic analytics method and an arrangement forimplementing the method on a computer. The traffic analytics method,which is hereinafter referred to as the traffic analytics method 310,can provide a network operator with tools to guide decisions aboutmigrating computing tasks to an alternative task performer such as aprivate cloud computing resource or a public cloud computing resourceand tools for automatically effecting the migration of selectedcomputing tasks to an alternative task performer.

“Cloud computing” refers to the access of computing resources and datavia a network infrastructure, such as the Internet. The computingresources and data storage may be provided by linked data centers of the“cloud,”—i.e., a “cloud” network. Each of the data centers may include aplurality of servers that provide computing resources, as well as datastorage and retrieval capabilities. As used herein. “cloud serviceproviders” refers to the owners or operators of the data centers thatoperate the “cloud” networks.

With reference now to FIG. 2, which is a perspective schematic view of anetwork, the traffic analytics method 310 will be described In furtherdetail including how the traffic analytics method 310 can be performedto evaluate the representative network shown in FIG. 2. As seen in FIG.2, a representative or exemplary network 220 is to be understood asrepresenting any information technology or “IT” arrangement operable tostore, manipulate, and present information to the network operator. Asseen in FIG. 2, the exemplary network 220 can comprise componentsenabling the network to operate as a local area network, a wide areanetwork such as the Internet, and/or a wireless network operable toreceive a wireless signal from a transmitter 222. The computerscomprised by the network 220 may include desktop computers 224, lap-topcomputers 226, hand-held computers 228 (including wireless devices suchas wireless personal digital assistants (PDA) or mobile phones), or anyother type of computational arrangement of hardware and/or software. Theseveral computers may be connected to the network 220 via a server 230.It should be noted that any other type of hardware or software may beincluded in the system and be considered a component thereof.

The traffic analytics method 310 can be deployed to evaluate the systemassets of any network that can broadly be considered to be acommunications network In that the network routes and handlescommunication signals and such a network can be configured as a wirelessnetwork or a wired network, or a combination wireless and wired network.In the context of the network being configured as a computer network,the network can include an entirely hardware inventory of assets, anentirely software inventory of assets, or an inventory of software andhardware assets. As is well known, a computer network may be a virtualnetwork in that a hardware asset hosts a plurality of virtual machines(VMs), each virtual machine operating as a stand alone computer andproviding a characteristic computer device function such as a serverfunction or a storage function. The host hardware asset typicallyincludes a management entity, often called “hypervisor,” that controlsand manages different virtual machines.

The traffic analytics method 310 is configured for use with a networkwhich, as schematically shown in FIG. 2, is designated as the in-placenetwork 220 and which is to be understood as representing anycommunication network, information technology network or “IT”arrangement operable to store, manipulate, and present information tothe network operator. As seen in FIG. 2, the in-place network 220 cancomprise components enabling the network to operate as a local areanetwork, a wide area network such as the Internet, and/or a wirelessnetwork operable to receive a wireless signal from a transmitter 222.The computers comprised by the network 220 may include desktop computers224, lap-top computers 226, hand-held computers 228 (including wirelessdevices such as wireless personal digital assistants (PDA) or mobilephones), or any other type of computational arrangement of hardwareand/or software. The several computers may be connected to the in-placenetwork 220 via a server 230. It should be noted that any other type ofhardware or software may be included in the system and be considered acomponent thereof.

With reference now to FIG. 2, which is a perspective schematic view of anetwork, the traffic analytics method 310 will be described in furtherdetail including how the traffic analytics method 310 can be performedto evaluate the representative network shown in FIG. 2. As seen in FIG.2, a representative or exemplary network 220 is to be understood asrepresenting any information technology or “IT” arrangement operable tostore, manipulate, and present information to the network operator. Asseen in FIG. 2, the exemplary network 220 can comprise componentsenabling the network to operate as a local area network, a wide areanetwork such as the Internet, and/or a wireless network operable toreceive a wireless signal from a transmitter 222. The computerscomprised by the network 220 may include desktop computers 224, laptopcomputers 226, hand-held computers 228 (including wireless devices suchas wireless personal digital assistants (PDA) or mobile phones), or anyother type of computational arrangement of hardware and/or software. Theseveral computers may be connected to the network 220 via a server 230.It should be noted that any other type of hardware or software may beincluded in the system and be considered a component thereof.

The traffic analytics method 310 can be deployed to evaluate the systemassets of any network that can broadly be considered to be acommunications network in that the network routes and handlescommunication signals and such a network can be configured as a wirelessnetwork or a wired network, or a combination wireless and wired network.In the context of the network being configured as a computer network,the network can include an entirely hardware inventory of assets, anentirely software inventory of assets, or an inventory of software andhardware assets. As is well known, a computer network may be a virtualnetwork in that a hardware asset hosts a plurality of virtual machines(VMs), each virtual machine operating as a stand alone computer andproviding a characteristic computer device function such as a serverfunction or a storage function. The host hardware asset typicallyincludes a management entity, often called “hypervisor,” that controlsand manages different virtual machines.

The traffic analytics method 310 is configured for use with a networkwhich, as schematically shown in FIG. 2, is designated as the in-placenetwork 220 and which is to be understood as representing anycommunication network information technology network or “IT” arrangementoperable to store, manipulate, and present information to the networkoperator. As seen in FIG. 2, the in-place network 220 can comprisecomponents enabling the network to operate as a local area network, awide area network such as the internet, and/or a wireless networkoperable to receive a wireless signal from a transmitter 222. Thecomputers comprised by the network 220 may include desktop computers224, laptop computers 226, hand-held computers 228 (including wirelessdevices such as wireless personal digital assistants (PDA) or mobilephones), or any other type of computational arrangement of hardwareand/or software. The several computers may be connected to the in-placenetwork 220 via a server 230. It should be noted that any other type ofhardware or software may be included in the system and be considered acomponent thereof.

The in-place network 220 schematically illustrated in FIG. 2 includesnetwork resources that are operable to distribute communication signalsthat transit to and between these network resources. Each networkresource has a transit relationship with a communication signal in thatthe network resource may originate a communication signal and/or receivea communication signal. Communication signals can be in any form as isknown in the art, including, as an example of a non-binary informationsignal, wireless radio wave signals or, as an example, of a binaryinformation signal, “packets”, which is intended to mean the discretecollection of information typically referred to by this term withrespect to signals handled by computer hardware devices. The handling ofa packet by a network is typically governed by a set of rules thatdefines its structure and the service it provides. As an example, theWorld Wide Web has a standard protocol referred to as the Hyper TextTransport Protocol (HTTP) and this standard protocol dictates howpackets are constructed, how data is presented to web servers, and howthese web servers return data to client web browsers. Any applicationthat transmits data over a computer network uses one or more protocols.There are typically numerous protocols in use between computers on anetwork.

The network resources of the in-place network 220 are shown, solely forthe sake of illustration, as including hardware assets in the form ofrouters, switches, servers operable to run Windows™ brand software,workstations operable to run Windows™ brand software, Linux/Unixservers, hosts operable to run VMware™ software, terminals, hubs,branches, intersections, and bridges.

The in-place network 220 includes entity applications that directdiscrete distributions of communication signals between several networkresources. An “entity application” is a term used in this specificationto refer to any form of application run on a network and may include agroup of software assets that operate cooperatively with each other toprovide a processing function or may include a single software asset,proprietary to the entity operating the network or bought or leased bythe entity, that provides a processing function. An “entity application”may include commercially available applications such as, for example,Windows™ brand software, Linux™ software, or VMware™ software, any formof freeware or shareware, or any form of custom software proprietary to,or licensed by, the network operator.

For purposes of further describing the traffic analytics method 310, thein-place network 220 is deemed to have a number of discrete collectionsof hardware assets with each discrete collection being located at adifferent geographic venue than the other discrete collections. Adiscrete collection of hardware assets may be comprised of a singlehardware asset or a plurality of hardware assets. Also, for the purposesof the present specification, the term “geographic venue” shall be takento refer to any geographic location or area that is identifiableutilizing any descriptor, metric or characteristic. The term “geographicvenue” shall accordingly be taken to include a continent, a country, astate, a province, a county, a city, a town, village, an address, aDesignated Marketing Area (DMA), a Metropolitan Statistical Area (MSA),a Primary Metropolitan Statistical Area (PMSA), location, zip or postalcode areas, and congressional districts. Additionally, “geographicvenue” can be defined in terms of country/city/state/address, countrycode/zip code, political region, geographic region designations,latitude/longitude coordinates, spherical coordinates, Cartesiancoordinates, polar coordinates, GPS data, cell phone data, directionalvectors, proximity waypoints, or any other type of geographicdesignation system for defining a geographical location or position.

Referring further to FIG. 1, it is to be understood that the steps ofthe traffic analytics method 310 can be performed in other differentsequences within the scope of the present invention. As seen in FIG. 1,a method performance step sequence 510 includes a step 520 of creatingan inventory collection of system assets and this step includesidentifying system assets of the network, the system assets includingone or both: (1) network resources that are operable to distributecommunication signals that transit to and between these networkresources with each network resource having a transit relationship witha communication signal in that the network resource may originate acommunication signal and/or receive a communication signal and (2)entity applications that direct distributions of communication signalsbetween several network resources. For the sake of illustration, networkresources are schematically shown in FIG. 1 in the form of a server 622,a shared uplink 624, a switch 626, and a router 628 and entityapplications are schematically shown in FIG. 1 as a virtualizationapplication 630, an accounting application 632, and a spreadsheetapplication 634.

The inventory collection created via the step 520 can be stored in anysuitable storage format and can be optionally displayed or not displayedin human-readable format. Moreover, the inventory collection may becreated via any suitable inventory collection approach including, forexample, via network discovery techniques capable of discovering systemassets in an existing network, via reference to a previously-generatedinventory collection, or via estimates of the presence or absence of thesystem assets of the network. Known techniques such as SMNP inventorycollection can be used.

The traffic analytics method 310 includes a step 530 of assigning, withregard to each of selected ones of the system assets, a locationassignment that classifies the system asset as having a locationassociated with a respective one of the network resources. Each networkresource, such as the server 622, the shared uplink 624, the switch 626,and the router 628, is, by virtue of being a hardware asset, situated ata respective geographic venue and reference is had to FIG. 3, which is aschematic illustration of a plurality of geographic venues each of whichhas a discrete collection of hardware assets variously situated atdifferent geographic venues (shown on a schematic globe 690). As seen inFIG. 3, the server 622 is comprised in the respective discretecollection of hardware assets situated in a city CI-AA, the shareduplink 624 is comprised in the respective discrete collection ofhardware assets situated in a city CI-BB, the switch 626 is comprised inthe respective discrete collection of hardware assets situated in a cityCI-CC, and the router 628 is comprised in the respective discretecollection of hardware assets situated in a city CI-DD.

A system asset in the form of an entity application can also be assigneda location assignment that classifies the entity application as having alocation associated with a respective one of the network resources. Anysuitable arbitrary assignment system can be used for this purposeincluding, for example, an assignment system that arbitrarily assigns aspecific network resources such as a server as the respective locationassignment of a particular entity application based upon thecharacteristic that the entity application is “hosted” on the server. Tobe sure, an entity application may be processed via a large multitude ofnetwork resources situated at various geographic venues. However, it mayoften be sufficient to arbitrarily assign a specific network resource asthe respective location assignment of a particular entity applicationbased upon some plausible criteria, for the reason that the trafficanalytics data yielded via the method of the present invention willstill be beneficial even though, as will be seen in the furtherdiscussion of the method herein, various ones of the network resourceshave been arbitrarily assigned as the respective location assignment ofa particular entity application.

With continuing reference to FIG. 1, the traffic analytics method 310includes a step 540 of ascribing to each communication signal of a baseset of communication signals, a touchstone tag that indicates that theso-tagged communication signal has a transit relationship with arespective one of the network resources. The step of ascribing atouchstone tag to each communication signal of the base set ofcommunication signals can be executed via anyone of a number of suitabletag ascribing approaches. For example, a touchstone tag can beindividually ascribed to each communication signal or a plurality oftouchstone tags can be ascribed in a groupwise-manner to a plurality ofcommunication signals. Moreover, the touchstone tag ascribed to acommunication signal can be dynamically ascribed which is a term usedherein to indicate that a touchstone tag is ascribed to a communicationsignal as a function of a detection that the communication signal hasactually originated from or been received by a respective networkresource (with the communication signal thus being ascribed a touchstonetag indicating a transit relationship with this respective networkresource). Alternatively, the touchstone tag ascribed to a communicationsignal can be ascribed in a non-dynamic manner which is a term usedherein to indicate that a touchstone tag is ascribed to a communicationsignal without regard to whether there has been a detection that thecommunication signal has actually originated from or been received by arespective network resource.

Ascribing a touchstone tag to a communication signal in a non-dynamicmanner may be performed, for example, based upon empirical dataindicating an earlier transit relationship of the communication signalwith the respective network resource. Furthermore, ascribing atouchstone tag to a communication signal in a non-dynamic manner may beperformed via an algorithm approach wherein a calculation of analgorithm is performed upon providing suitable variables andnon-variables for the algorithm. The variables and non-variables for thealgorithm needed not mandatorily be based upon empirical data relatingto communication signals traffic but can, instead, be variables ornon-variables determined via any suitable estimation technique. It is tobe understood that the terms “tag” or “tagging” are intended toencompass all manners of associating a communication signal with anetwork resource with which the communication signal has, or is believedto have, a transit relationship and neither term is to be limited by anyrequirement that information relating to the associating of acommunication signal with a network resource must be physically coupledwith the communication signal. Also, the terms “tag” or “tagging” areintended to encompass any construct for associating a communicationsignal with a network resource including a “pseudo-tag” construct viawhich a communication signal is deemed to have a transit relationshipwith a network resource solely for the purpose of facilitating thedeployment of the traffic analytics method 310, even if no informationexists that the communication signal has, in fact, a transitrelationship with the respective network resource.

While the applicant does not wish to be bound by any particular approachto associating a communication signal with a network resource with whichthe communication signal has, or is believed to have, a transitrelationship, it is noted that there exists a construct for designationlogical addresses and physical addresses associated with a network andit is within the scope of the present invention that reference can behad to this construct in connection with selecting an approach forassociating a communication signal with a network resource. An IPaddress is a 32-bit number or a 128-bit number assigned to a computer ordevice on the network that is unique to that network. A MAC address is a48-bit number that has been hardwired into the network adapter whichconnects a computer or device to the network, this number beinguniversally unique. Generally speaking, a MAC address is a unique codepermanently assigned to a particular piece of networking hardware, whilea machine name is another identifier of a computer on a network. Themachine name may be assigned by the network operator to the computerwhen the computer is coupled to the network.

Logical addresses, such as IP addresses, may be analyzed for inferringthat network traffic relates to a particular client. Because logicaladdresses change, locating a computer assigned to the IP address may beperformed by sending look-up requests to a Dynamic Host Control Protocol(DHCP) server and/or an Active Directory server on the network. Instead,physical addresses, such as MAC addresses, may be used to infer thatnetwork traffic has transmitted a particular computer.

The traffic analytics method 310 further includes a step 550 ofanalyzing the communication traffic comprised of touchstone-taggedcommunication signals having transit relationships with the networkresources of a first set of network resources 640 and a second set ofnetwork resources 642. The first resource location group may be anyarbitrarily chosen set of network resources that have a locationattribute in common with one another and the second resource locationgroup may be any arbitrarily chosen set of network resources that have alocation attribute in common with one another, with the first set ofnetwork resources and the second set of network resources beingdifferent from one another. The step 540 of analyzing the signals havingtransit relationships with the network resources of the first set ofnetwork resources and the second set of network resources, is performedfor the purpose of deriving traffic analytics data from the analysis644. The traffic analytics data 644 can be displayed, if desired, on agraphical user interface 646 and/or can be presented in any suitablehuman readable form.

The traffic analytics data 644 yielded by the traffic analytics method310 may directly indicate certain performance characteristics of thein-place network 220 and/or may permit conclusions or inferences aboutthe in-place network 220 to be drawn. For example, the traffic analyticsdata 644 may reveal that the system assets at a geographic venue ofinterest are significantly involved in the running of certain entityapplications. For example, the traffic analytics data 644 may indicatethat certain network resources at a particular geographic venue areroutinely tapped by the hypervisor manager to handle significantvirtualization tasks. The traffic analytics data 644 provides valuableperformance information about the in-place network 220 because thetraffic analytics data is harvested via a “flow-to-flow” approach inwhich the paths of the communication signals are analyzed independent ofthe static or prescribed traffic pattern that may be surmised based onlyupon the stated capacities and/or initial set up of the system assets.

The traffic analytics data 644 is qualitatively different than datatypically obtained via known methods such as, for example, charting ordiscovery techniques that analyze the transmission of packets alongend-to-end paths of network connections. Such techniques fail to providethe richer information about the actual packet transmission paths thatcan be provided by the traffic analytics method 310.

As can be appreciated, the traffic analytics method 310 canadvantageously provide insights into the performance of the in-placenetwork 220 and these performance insights can be leveraged to assistthe operator of the in-place network 220 to make information technology(IT) management decisions such as, for example, decisions relating toreplacing or upgrading system assets and including decisions aboutreplacing some or all of the functionality of system assets viamigration of selected network functions to a location external of thenetwork—i.e., migration to a public or private cloud. As an example ofthe feature of performing the traffic analytics method 310 to assist ina cloud migration decision, further reference is had to FIG. 1, whereinit can be seen that the traffic analytics method 310 may optionallyinclude a step 560 of assessing the traffic analytics data relative to aselection of migration scenarios each of which is a scenario whereinsome or all of the functions performed by some or all of the systemassets were instead to be performed at a location external to thenetwork. Furthermore, as an example of the value to a network operatorof performing the step 560 of assessing the traffic analytics datarelative to a selection of migration scenarios, it can be further seenin FIG. 1 that the traffic analytics method 310 can also optionallyinclude a step 570 of generating a migration consideration set comprisedof cost information units that each include cost information about oneof the migration scenarios.

The traffic analytics data 644 will be in many circumstances asufficient proxy of the actual inputs/outputs (I/O) of the in-placenetwork 220 that the operator of the in-place network 220 canconfidently make IT management decisions to a substantial degree as if acomplete real-time snapshot of the actual inputs/outputs (I/O) of thein-place network 220 had, in fact, been constructed. Advantageously, thetad can typically be generated at a significantly lower level of effortand cost than the effort and cost required to assemble a completereal-time snapshot of the actual inputs/outputs (I/O) of the in-placenetwork 220. Moreover, the traffic analytics data 644 provides a datareference base from which a visualization of the in-place network 220can be constructed and this visualization can be a convenient quickreference tool for the operator of the in-place network 220 in making ITmanagement decisions. For example, the traffic analytics data 644 canserve as a data reference base from which to construct a server centricvisualization which features a node/server centric visualization of thein-place network 220 including showing which servers a particular hostis communicating with and which protocols it is using to communicate onthe network with those servers. Alternatively, the traffic analyticsdata 644 can serve as a data reference base from which to construct anapplication centric visualization that shows which protocols aparticular host is using to communicate on the network with otherservers.

Returning again to further details of the step 540 of analyzing thecommunication traffic, it has been noted that the analysis of thecommunication traffic can be focused on communication signals havingtransit relationships with the network resources of the first set ofnetwork resources and the second set of network resources in order toderive traffic analytics data 644. Additionally, or alternatively, theanalysis of the communication traffic can be focused on a difference ordifferences in the nature of communication signals flowing between afirst inter-resource set (such as the first set of network resources andthe second set of network resources) and communication signals flowingbetween a second inter-resource set (i.e., the second set of networkresources and a third set of network resources that is different setthan either the first set of network resources and the second set ofnetwork resources).

The analysis of the traffic analytics data 644 may be conducted to yieldany suitable information about the in-place network 220. In thisconnection, it is believed that traffic analytics data yielded by thetraffic analytics method 310 can often advantageously provide at leasttwo types of yielded information that are probative with regard toassisting decisions about migrating some or all of the functionscurrently performed by some or all of the system assets to a locationexternal to the network. The term “probative” as used herein is intendedto mean that the yielded information may reveal that a particularmigration scenario may be difficult or impractical or, conversely, thata particular migration scenario may be advantageous or helpful. The twotypes of yielded information noted as being probative are: (a)information about the volume of communication traffic to and betweenselected geographic venues (i.e., how much communication traffic existsbetween those geographic venues) and (b) the types of network resourcesinvolved in handling this communication traffic. Other types of yieldedinformation will be advantageous as well and can serve the goals ofproviding a display of infrastructure assets and current performancelevels, providing visibility about network components including network,virtualization, and Windows servers, guiding or improving the accuracyof predictions about infrastructure-as-a-Service (IaaS) components thatmay be needed from a variety of components providers, and permitting thenetwork operator to clearly understand how much IaaS solutions will costin a cloud model as compared to traditional delivery models.

In connection with the step 560 of assessing the traffic analytics datarelative to a selection of migration scenarios and the step 570 ofgenerating a migration consideration set comprised of cost informationunits, the migration consideration set may optionally include aplurality of cloud service providers who are deemed capable ofperforming a network computing task in lieu of the task being performedby the network and may further comprise a price qualified roster eachmember of which is a cloud service provider: (a) whose price forperforming the task complies with a price acceptability criteria or (b)that has a respective price proposal associated therewith reflecting aprice for engaging the cloud service provider to perform the task inlieu of the task being performed by the network. Moreover, this pricequalified roster can be displayed in a format suitable for the networkoperator to, at the least, have a display of each alternative taskperformer in the price qualified set and its respective price proposalthat reflects a price for engaging the alternative task performer toperform the task in lieu of the task being performed by the network. Itis to be understood that the term “display” is used in a broad sense andencompasses all forms of communication in visual, aural, and tactileformat and including both human- and machine-interface variations.

The method of the present invention can be executed manually but ispreferably executed via a tangible computer-readable medium forcontrolling a computing device to generate an output. This tangiblecomputer-readable medium may be connected (e.g., networked) to othermachines in a Local Area Network (LAN), an intranet, an extranet, or theInternet. The tangible computer-readable medium may operate in thecapacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The tangible computer-readable medium may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines (e.g., computers) that individuallyor jointly execute a set (or multiple sets) of instructions to executethe steps of the method of the present invention.

The user interface via which the price qualified roster is accessible toa network operator may be configured as a stand-alone module that can beprovided at no cost, at cost, or via leasing to network operators,wherein this stand-alone module may be configured to implement themethod for facilitating network external computing assistance onceoperationally connected to a network. Alternatively, the user interfacevia which the price qualified roster is accessible to a network operatormay be configured and controlled by an intermediary agent which isfiguratively between the network operators seeking migration guidanceand the cloud service providers. The intermediary agent may configurethe user interface and may provide the ancillary services that harvestthe data to be handled during the implementation of the method forfacilitating network external computing assistance, such as, forexample, the network discovery services that identify and catalog theassets of the network. Additionally, the intermediary agent may solicit,coordinate, and structure the display artifacts via which informationabout the “sponsoring” and “non-sponsoring” cloud service provider isprovided to the network operators. In this connection, the intermediaryagent may solicit participation from potential “sponsoring” cloudservice provider by negotiating or prescribing a price offering menu,for example, or the intermediary agent may conduct a bidding processamong potential “sponsoring” cloud service provider and award successfulbid presenters with preferred listing opportunities, guaranteed minimumnumber of listing presentations, etc.

As seen in FIG. 4, which is a schematic illustration of a display thatmay be configured on, and configured via, a dashboard platform fordisplaying information about the price qualified rosters obtained viathe method of the present invention, such a display may be configuredon, and configured via, a dashboard platform 470 and the dashboardplatform 470 may be configured to provide an intentionally orderedpresentation of the alternative task performer or performers in theprice qualified roster with such presentation being communicatedvisually and/or aurally to the network operator. The intentionallyordered presentation of the alternative task performer or performers inthe price qualified roster may be arranged, for example, so as toprovide the network operator with a hierarchal listing of thealternative task performer or performers in the price qualified roster.As another example, the alternative task performer or performers in theprice qualified roster may be so arranged, for example, so as to providethe network operator with a hierarchal listing of the alternative taskperformer or performers in the price qualified roster based upondirecting the network operator to preferred resources that can helpresolve metrics issues or capitalize upon identified opportunities.Further in this connection, the preferred resources can be comprised ofvendors who have a particular capability such as, for example, cloudservice providers having a particular capability, or vendors who aregiven preference relative to other vendors based upon a sponsorshipcriteria (i.e., “sponsoring” vendors are given a preferential showing inthe display provided by the dashboard platform 470 as opposed to“non-sponsoring” vendors).

The exemplary format displayed by the dashboard 470 in FIG. 4 includes a“banner”-type listing 484 comprising information about a “sponsoring”cloud service provider and this “banner”-type listing 484 is presentedin a text and graphics format. The “banner”-type listing 484 ispresented as the topmost one of a vertical listing of the cloud serviceproviders in the price qualified roster. For the purposes ofillustration, it is assumed that the other cloud service providers 486listed below the “banner”-type listing 484 are “non-sponsoring” cloudservice providers. Information about the other cloud service providers486 listed below the “banner”-type listing 484 are displayed in atext-only format or in another format selected to be less noticeablethan the “banner”-type listing 484 comprising information about a“sponsoring” cloud service providers. Thus, by virtue of its topmostlisting on the vertical listing of vendors and its more noticeablepresentation in both text and graphics as opposed to text alone, the“banner”-type listing 484 comprising information about a “sponsoring”cloud service providers is given a preferential showing in the displayprovided by the dashboard platform 470 as opposed to the “non-sponsored”cloud service providers 486. “Banner”-type listings may be comprisedsolely of graphic, graphics and text, or text only and may include, forexample, rich media (audio video), promotions, or any feature thatincreases the value of listing to a vendor or a potential vendor willingto give value in return for a status as a “sponsoring” vendor.

In accordance with one variant for displaying the price qualifiedroster, with particular application in the context of cloud serviceproviders who agree to be “sponsoring” cloud service providers, cloudservice providers may create and manage their listings through a userinterface that permits the creation of one or more listing formats, theselection of details to associate with the listings, and an inserttemplate that governs how and in which situations a listing will bedisplayed to a network operator. The cloud service provider may enteradditional pieces of information and functionality pertaining to eachlisting. The cloud service provider may choose that a given listing betargeted only a discrete group of network operators, such as thosemeeting a set of customer demographics. Cloud service providers may setthe specific price at which they are willing to offer their cloudservices such as, for example, a single price or a price range for a“computing unit.” If desired, cloud service providers may have theability to be apprised of the price offerings of other cloud serviceproviders in order, for example, to ensure that their listing appears ina desired position or to optimize the “click-through” response orcontact response of the listing.

An algorithm is here, and generally, conceived to be a self-consistentsequence of steps leading to a desired result. The steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared and otherwise manipulated by a computer system. It will beconvenient at times, principally for reasons of common usage, to referto the above-referenced signals as bits, values, elements, symbols,characters, terms, numbers or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities.

Unless specifically stated otherwise, it will be appreciated thatthroughout the description of the present invention, use of terms suchas “processing”, “computing”, “calculating”, “determining”, “displaying”or the like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.Further, various embodiments of the present invention may be implementedwith the aid of computer-implemented processes or methods (a.k.a.programs or routines) that may be rendered in any computer languageincluding, without limitation, C#, C/C++, Fortran, COBOL, PASCAL,assembly language, markup languages (e.g., HTML, SGML, XML, VoXML), andthe like, as well as object-oriented environments such as the Javaobject-oriented environment and the like. In general, however, all ofthe aforementioned terms as used herein are meant to encompass anyseries of logical steps performed in a sequence to accomplish a givenpurpose.

The present invention can be implemented with an apparatus to performthe operations described herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise ageneral-purpose computer, selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer readable storage medium, such as, but not limitedto, any type of disk including floppy disks, optical disks. CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and processes presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method. For example, any of themethods according to the present invention can be implemented inhard-wired circuitry, by programming a general-purpose processor or byany combination of hardware and software.

One of ordinary skill in the art will immediately appreciate that theinvention can be practiced with computer system configurations of anytype, including hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, DSP devices,network PCs, minicomputers, mainframe computers, personal computers, andthe like. The invention can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network.

One variation of the method for gathering traffic analytics data about acommunication network is suitable for analyzing traffic analytics dataabout a communication network that includes a plurality of nodes and aplurality of connections between the nodes. Reference is had to FIG. 5,which is a schematic plan view of a portion of the nodes of a network.Each node of the network shown in FIG. 5 is one of the system assets ofthe network and, for purposes of illustrating an exemplary performanceof the method, each node can be understood to be in the form of anyknown system asset of a communication network that is capable ofcommunicating with another node of the network. The network shown inFIG. 5 includes a first group of nodes 740, 742, 744, 746, and 748, asecond group of nodes 750, 752, 754, 756, and 758, and a third group ofnodes 760, 762, 764, 766, and 768.

The method includes performing a step of cataloging an initial locationand this step includes identifying connectivity relationships betweenthe nodes of a first group of nodes of the network, the identifiedconnectivity relationships delimiting a first connectivity relationshipsset. As seen in FIG. 5, solely for the sake of illustration, a firstgroup of nodes are arbitrarily allocated as being comprised of the nodes740, 742, 744, 746, and 748. As will become clear, the method yieldsbeneficial results no matter what arbitrary selection criteria isdeployed to arbitrarily allocate certain nodes to the first group ofnodes—with the consequent result that the non-allocated nodes that havenot been allocated to this first group of nodes. The task of identifyingconnectivity relationships between the nodes of this first group ofnodes of the network can be accomplished via any suitable approach foridentifying connections between the nodes of a network. For example,here is a partial listing of several well known sources that can beconsulted for gathering information about connections between the nodesof a network: a Common Data Dictionary [CDD], Low Level Direct Drive[LLDD], software-defined networking (SDN) databases, spanning trees,routing neighbors. MAC tables, VPN configurations, Logical Unit Numbers(LUNs), fiber cable (FC) topologies, and unified switch configurations.Data about connections between the nodes of a network can be normalizedas needed or desired. After the step of cataloging an initial locationhas been performed, a next step of the method is then performed asfollows. With respect to each identified connectivity relationship ofthe first relationship set, the method includes determining if therespective identified connectivity relationship is in compliance with atarget array formation heuristic, whereupon each connectivityrelationship of the first connectivity relationship set is cataloguedinto a respective one of a target array comprised of those connectivityrelationships in compliance with the target array formation heuristicand a non-target array comprised of those connectivity relationships notin compliance with the target array formation heuristic. Reference ishad to FIG. 6, which is a schematic elevational view of aconceptualization of an Initial portion of this determining step. Atarget array formation heuristic is a rule that stipulates a givencharacteristic or property that all members of a target array mustpossess to be deemed to be a member of the target array. For example,the target array formation heuristic may be a rule that none of thenodes of the respective group of nodes has a connection to a node thatis at a different level as classified in the Open SystemsInterconnection (OSI) seven-layer model, which had its origin in areference model created by the International Standards Organization as astandard ISO/IEC 7498. system. As seen in FIG. 6, the target arrayformation heuristic 820 is applied to determine which, if any, of theconnectivity relationships of the first connectivity relationship setare in compliance with the target array formation heuristic.

The first connectivity relationship set, generally designated as thefirst connectivity relationship set 830, includes a first plurality ofconnections 832, a second plurality of connections 834, and a thirdplurality of connections 836. For the sake of illustration, only thefirst plurality of connections 832 of the first connectivityrelationship set 830 are seen to be in compliance with the target arrayformation heuristic 820—that is, only the first plurality of connections832 of the first connectivity relationship set 830 satisfy the rule thatthe respective connection between a given node and another node is aconnection of the given node with a node that is at a different level asclassified in the Open Systems Interconnection (OSI) seven-layer model.The second plurality of connections 834 and the third plurality ofconnections 836 are deemed, for the sake of illustration, as not beingin compliance with the target array formation heuristic 820 (i.e., thenodes that are connected via the second plurality of connections 834 andthe third plurality of connections 836 have a connection with a nodethat is at a different level as classified in the Open SystemsInterconnection (OSI) seven-layer model). The second plurality ofconnections 834 and the third plurality of connections 836 are thusdeemed to be members of a non-target array comprised of thoseconnectivity relationships not in compliance with the target arrayformation heuristic 820, as seen in FIG. 7, which is a schematicelevational view of a conceptualization of this portion of thedetermining step.

The method thereafter includes a designating step with respect tocertain nodes of the first group of nodes of the network. Specifically,with respect to those nodes of the first group of nodes of the networkwhose connectivity relationships are in compliance with the target arrayformation heuristic, the step involves designating such nodes with acommon location designator that is unique from other locationdesignators that have been associated, via a step of cataloging alocation, with nodes of the network. For example, as seen in FIG. 8,which is a schematic elevational view of a conceptualization of thisdesignating step, those nodes of the first group of nodes of the networkwhose connectivity relationships are In compliance with the target arrayformation heuristic 820—namely, those nodes of the first group of nodesof the network whose connectivity relationships are the first pluralityof connections 832 of the first connectivity relationship set 830—aredesignated with a common location designator “LOC ONE” that is uniquefrom all other location designators. The node 740 and the node 746 ofthe first group of nodes of the network are representatively shown inFIG. 8 as representing those nodes whose connectivity relationships arethe first plurality of connections 832.

The method then includes performing a step of cataloging a subsequentlocation and this step includes, with respect to a subsequent group ofnodes of the network that is different from other groups of nodes of thenetwork that have been subjected to a location cataloging step of themethod, identifying connectivity relationships between the nodes of thesubsequent group of nodes, the identified connectivity relationshipsdelimiting a subsequent connectivity relationship set, and the step ofidentifying connectivity relationships between the nodes of thesubsequent group of nodes including considering only those connectivityrelationships that have not previously been designated with a locationdesignator during a step of cataloging a location. Thus, a subsequentgroup of nodes of the network that is different from other groups ofnodes of the network that have been subjected to a location catalogingstep of the method—namely, a subsequent group of nodes of the networkthat is different from first groups of nodes of the network that hasbeen subjected to a location cataloging step of the method—is nowidentified and, as before, the method yields beneficial results nomatter what arbitrary selection criteria is deployed to arbitrarilyallocate certain nodes to the subsequent group of nodes—with theconsequent result that the other nodes are not allocated to thissubsequent group of nodes.

The method further includes, with respect to each identifiedconnectivity relationship of the subsequent connectivity relationshipset, determining if the respective identified connectivity relationshipis in compliance with the target array formation heuristic, whereuponeach connectivity relationship of the subsequent connectivityrelationship set is catalogued into a respective one of the target arraycomprised of those connectivity relationships in compliance with thetarget array formation heuristic and the non-target array comprised ofthose connectivity relationships not in compliance with the target arrayformation heuristic. Thus, each identified connectivity relationship ofthe subsequent connectivity relationship set is evaluated to determineif it is in compliance with the target array formation heuristic 820.Thereafter, the method thereafter includes a designating step withrespect to certain nodes of the subsequent group of nodes of thenetwork. Specifically, with respect to those nodes of the subsequentgroup of nodes of the network whose connectivity relationships are Incompliance with the target array formation heuristic 820, the stepinvolves designating such nodes with a common location designator thatis unique from other location designators that have been associated, viaa step of cataloging a location, with nodes of the network. For example,those nodes whose connectivity relationships are in compliance with thetarget array formation heuristic 820 can be designated with a commonlocation designator “LOC TWO” that is unique from other locationdesignators (i.e., it is different than the common location designator“LOC ONE” and all other location designators).

The method includes repeating the step of cataloging a subsequentlocation until at least two nodes of the network have been designatedwith a location designator. Thus, the step of cataloging a subsequentlocation can be iterated until all the nodes of a given portion of anetwork, or the entirety of the nodes of the network, have beencataloged such that each node is associated with a respective commonlocation designator such as, for example, the common location designator“LOC ONE” or the common location designator “LOC TWO.”

The method further includes analyzing the communication traffic byreferring to the location designators and deriving traffic analyticsdata from the analysis. As can be understood, benefits can be derived inthe context of traffic analytics data via exploitation of the datayielded via performance of the method of the present invention. Forexample, a person desiring to determine possible migration strategies inwhich the functions of some or all of the system assets of a network areshifted to a cloud based operation can obtain a ready picture of thosesystem assets of the network whose functions could be shifted to a cloudbased operation in a manner that reduces the risk that other systemassets of the network whose functions have not been shifted to the cloudbased operation will still operate smoothly. For example, a person canobtain a ready picture of those system assets of the network that do nothave an “inter-level” connection (a connection between a node at onelevel as classified In the Open Systems Interconnection (OSI)seven-layer model) and a node at a different level as classified in theOpen Systems Interconnection (OSI) seven-layer model), whereupon, insome circumstances, the ability to shift only the functions of thosesystem assets of the network that do not have an “inter-level”connection to a cloud based operation may advantageously reduce the riskthat the functioning of non-shifted nodes of the network will beinadvertently impaired due to, say, the shut down of those “shifted”system assets of the network whose functions have been shifted to thecloud based operation.

We claim: 1-13. (canceled)
 14. A method comprising, monitoringcommunications among networked resources of a networked environment,wherein the networked resources includes a plurality of hardware assetsand a plurality of applications; using information of the monitoredcommunications to assign the plurality of hardware assets and theplurality of applications to geographic locations; the monitoringcommunications among the networked resources including taggingcommunications among the networked resources, wherein the taggingincludes identifying originating network resources of the taggedcommunications and target network resources of the taggedcommunications, wherein networked resources include the originatingnetwork resources and the target network resources; using the taggedcommunications to identify utilization of the networked resources at oneor more locations of the geographic locations.
 15. The method of claim14, the assigning the plurality of hardware assets and the plurality ofapplications to geographic locations including assigning a firstcollection of hardware assets and applications to a first location ofthe geographic locations.
 16. The method of claim 15, the assigning theplurality of hardware assets and the plurality of applications togeographic locations including assigning a second collection of hardwareassets and applications to a second location of the geographiclocations, wherein the first location is different than the secondlocation.
 17. The method of claim 1, the monitoring communications amongthe networked resources including gathering information ofcommunications among the networked resources using one or more networkinformation sources.
 18. The method of claim 17, the one or more networkinformation sources comprising a Common Data Dictionary.
 19. The methodof claim 17, the one or more network information sources comprising aLow Level Direct Drive.
 20. The method of claim 17, the one or morenetwork information sources comprising software-defined networkingdatabases.
 21. The method of claim 17, the one or more networkinformation sources comprising spanning trees.
 22. The method of claim17, the one or more network information sources comprising routingneighbors.
 23. The method of claim 17, the one or more networkinformation sources comprising MAC tables.
 24. The method of claim 17,the one or more network information sources comprising VPNconfigurations.
 25. The method of claim 17, the one or more networkinformation sources comprising Logical Unit Numbers.
 26. The method ofclaim 17, the one or more network information sources comprising fibercable (FC) topologies.
 27. The method of claim 17, the one or morenetwork information sources comprising unified switch configurations.28. The method of claim 1, wherein the identifying utilization of thenetworked resources comprises identifying utilization of at least oneapplication of the plurality of applications.
 29. The method of claim 1,wherein the identifying utilization of the networked resources comprisesidentifying client applications of the plurality of applicationutilizing the at least one application of the plurality of applications.30. The method of claim 1, wherein the identifying utilization of thenetworked resources comprises recommending migration of a portion of thenetworked resources to a cloud computing platform.